Abstract
The modern development of ultra-durable and energy-efficient IoT based communication sensors has much application in modern telecommunication and networking sectors. Sensor calibration to reduce power usage is beneficial to minimizing energy consumption in sensors as well as improve the efficiency of devices. Reinforcement learning (RL) has been received much attention from researchers and now widely applied in many study fields to achieve intelligent automation. Though various types of sensors have been widely used in the field of IoT, rare researches were conducted in resource optimizing. In this novel research, a new style of power conservation has been explored with the help of RL to make a new generation of IoT devices with calibrated power sources to maximize resource utilization. A closed grid multiple power source based control for sensor resource utilization has been introduced. Our proposed model using Deep Q learning (DQN) enables IoT sensors to maximize its resource utilization. This research focuses solely on the energy-efficient sensor calibration and simulation results show promising performance of the proposed method.
Highlights
Internet of Things (IoT) has wide usage and is crucial to our daily life
We have studied the technology for sensor data control with Reinforcement learning (RL) algorithms
This along with the deep neural network architecture provides a huge boost up in the model to learn from the simulation. the detailed of the model and ensemble strategy is discussed detailed in the deep q learning subsection
Summary
Internet of Things (IoT) has wide usage and is crucial to our daily life. Application of real-time monitoring and activity detection has gained a significant role in modern-day IoT sensors. Much research has been focused to solve complex problems with reinforcement learning, the energy-saving mechanism of the IoT devices with the reinforcement model has not been properly explored. The energy utilizing a section for IoT sensors with deep reinforcement learning is a promising sector for such a domain to apply reinforcement learning solutions. Much research focused on visual puzzle solving, using the deep reinforcement learning in the IoT sensors to utilize the energy resources is not properly explored. In this research, we have purposed some control based simulation for energy-efficient resource management to train with both deep learning algorithms and RL algorithms and compared the result. The study has proposed a new framework for simulating intelligent control and designed new simulation for testing energy-efficient models, especially for IoT sensor-based scenarios.
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